Typical approaches for matching objects from different images are typically descriptor based. That is, they are designed to detect things containing sufficient information to be considered interesting and unique within the scene. From any such object, features are extracted which allow calculation of a description vector that contains sufficient information to identify the object within the scene. The particular description vectors are selected to be invariant to expected transformations of the scene. That is, for an expected transformation, the derived description vector should remain a valid representation for describing the object, despite the transformation. One fundamental problem with the description vector approach is that to design a description vector that fulfills some desired properties, other must be given up. For example, it is not possible to produce a description vector that is generically invariant to all transformations. Instead, you must choose a particular set of transformations that you want to be invariant to. The more generic the description vector becomes, a greater number of objects in the scene will appear similar. This weakens the ability of a description vector to uniquely describe a single object. The more generic the description vector, the less descriptive it becomes. The design of the description vector must therefore be tuned to a specific situation, rendering its usefulness very limited in other situations. There are cases, thought, when descriptor based methods cannot be used. Other approaches using relational matching techniques as opposed to description vectors have been proposed. To date however, approaches using relational matching techniques have been limited in their ability to reliably handle three-dimensional real world scenes.
For the reasons stated above and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the specification, there is a need in the art for improved systems and methods for matching scenes.